Multivariate qualitative methodology for semi-quantitative information. A case study: Adulteration of olive oil with sunflower oil

Abstract

This paper proposes a strategy to assess the performance of a multivariate screening method for semi-quantitative purposes. The adulteration of olive oil with sunflower oil was considered as a case study using fluorescence spectroscopy and two-class Partial Least Squares Discriminant Analysis (PLS-DA). Building the proper screening methodology based on two-class multivariate classification model involve setting the cut-off value for the adulterated class (class 2). So, four classification models were established for four levels of adulterant (cut-off). Model validation involved calculating the main quality parameters (sensitivity, specificity and efficiency) and three additional semi-quantitative parameters (limit of detection, detection capability and unreliability region).The probability of successfully recognizing non-adulterated samples as such was set by the main performance parameters of the two-class model. However, the probability of successfully recognizing adulterated samples as such was more accurately extracted from the performance characteristic curves (PCC) curves instead of just from the sensitivity of the adulterated class.The main performance parameters of the PLS-DA models increased as the cut-off level increased although after a particular value the increase was less pronounced. As an example, when the cut-off was changed from 5% to 20%, sensitivity changed from 70 to 93%, specificity changed from 87 to 97%, and efficiency changed from 78 to 95%. The same can be stated for the semi-quantitative parameter's decision limit and detection capability, which moved from 0 to 1.6 and from 17.7 to 21.6 (% of adulterant), respectively.